Tensor Space Models for Authorship Identification

  • Spyridon Plakias
  • Efstathios Stamatatos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5138)


Authorship identification can be viewed as a text categorization task. However, in this task the most frequent features appear to be the most important discriminators, there is usually a shortage of training texts, and the training texts are rarely evenly distributed over the authors. To cope with these problems, we propose tensors of second order for representing the stylistic properties of texts. Our approach requires the calculation of much fewer parameters in comparison to the traditional vector space representation. We examine various methods for building appropriate tensors taking into account that similar features should be placed in the same neighborhood. Based on an existing generalization of SVM able to handle tensors we perform experiments on corpora controlled for genre and topic and show that the proposed approach can effectively handle cases where only limited training texts are available.


Authorship identification Tensor space representation Text categorization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Spyridon Plakias
    • 1
  • Efstathios Stamatatos
    • 1
  1. 1.Dept. of Information and Communication Systems Eng.University of the AegeanKarlovassiGreece

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